Microsoft Fabric Updates Blog

Fabric Change the Game: Exploring the data

Microsoft Fabric offers a set of different tools to explore and prepare the data for analysis and from that perspective, notebooks have become one of the quickest ways to get started with data exploration. This post draws its inspiration from the world of experimentation, exploration, and seamless integration into Microsoft Fabric- Data Science in Microsoft … Continue reading “Fabric Change the Game: Exploring the data”

Sending data to Synapse Real-Time Analytics in Fabric from Apache Kafka Ecosystems using Java

Introduction This quick start is based on the official Azure Event Hubs for Kafka example, adapted to work with Microsoft Fabric. In this blog we will show you how to send data from Kafka to Synapse Real-time Analytics in Fabric. Get the sample source code from GitHub fabric-kafka-sample You will use a Fabric Eventstream to … Continue reading “Sending data to Synapse Real-Time Analytics in Fabric from Apache Kafka Ecosystems using Java”

Data Warehouse Utilization Reporting in Fabric Capacity Metrics App

As announced today, we are excited to share that compute utilization reporting for Synapse Data Warehouse features: Warehouse and SQL Endpoint of the Lakehouse are now available in Public Preview for Microsoft Fabric in all regions! In the capacity-based SaaS world of Microsoft Fabric, you can purchase a Fabric SKU which comes with a set … Continue reading “Data Warehouse Utilization Reporting in Fabric Capacity Metrics App”

Microsoft Fabric Event Streams: Generating Real-time Insights with Python, KQL and Power BI

In the Age of AI (artificial intelligence), the ability to analyze, monitor and act on real-time data is becoming crucial for companies that wish to remain competitive in their industries. This increasing demand for real-time insights has led to the necessity for a fully managed service capable of handling high volumes of data ingestion and … Continue reading “Microsoft Fabric Event Streams: Generating Real-time Insights with Python, KQL and Power BI”